AI Workflow Authorization Template

You've built AI workflows that read support tickets, parse GitHub issues, or process form submissions. But these workflows might be letting unauthenticated users trigger privileged actions in your enterprise systems.

The issue isn't prompt injection. It's that your workflow executes with its own identity, not the requester's. When a CFO emails a request for financial data, the workflow uses the same service account as when an external attacker sends the identical request through your support inbox.

This template provides a reusable authorization wrapper that enforces identity-aware access control before any AI workflow executes downstream actions.

Purpose of the Template

This template creates an authorization checkpoint between your AI agent's decision and the privileged action it wants to execute. It propagates the original requester's identity through the workflow and validates their permissions before allowing database queries, API calls, or system modifications.

Use it when:

• Your AI workflows execute actions using service accounts or developer API keys.
• Unauthenticated entry points (support forms, public repos, shared documents) can trigger workflows.
• The same prompt from different users should produce different authorization outcomes.
• You need to prove who authorized each action for compliance reporting.

Prerequisites

Before implementing this template, ensure you have:

Identity context capture
Your workflow must extract and preserve the requester's identity from the entry point (email sender, authenticated session, API caller). For unauthenticated channels, you need a mechanism to reject or route requests to manual review.

Policy Decision Point
You need a system that can evaluate "Can user X perform action Y on resource Z?" This could be your existing IAM platform, a Policy-Based Access Control engine, or custom authorization logic that checks group membership and resource permissions.

Execution isolation
Your workflow runtime must support passing user context to downstream operations. If you're calling internal APIs, they must accept and honor an "on-behalf-of" parameter. If you're querying databases, you need row-level security or dynamic credential selection based on the requester.

Audit trail
You must log the original requester identity, the action requested, the authorization decision, and the identity used for execution. Without correlation between these elements, unauthorized access looks identical to legitimate automation.

The Authorization Wrapper Template

Insert this logic between your AI agent's output and any privileged operation:

def execute_with_authorization(
    requester_identity,
    ai_generated_action,
    target_resource
):
    """
    Enforce identity-aware access control for AI workflow actions.
    
    Args:
        requester_identity: Identity of the user who triggered the workflow
        ai_generated_action: The operation the AI agent determined to execute
        target_resource: The system, database, or API being accessed
    
    Returns:
        Execution result if authorized, denial response otherwise
    """
    
    # Step 1: Validate requester identity exists
    if not requester_identity or requester_identity == "anonymous":
        log_security_event(
            event_type="workflow_authorization_failure",
            reason="unauthenticated_requester",
            action=ai_generated_action,
            resource=target_resource
        )
        return {
            "status": "denied",
            "reason": "Authentication required"
        }
    
    # Step 2: Query authorization policy
    authorization_decision = policy_decision_point.evaluate(
        [subject](/glossary/subject)=requester_identity,
        action=ai_generated_action.operation_type,
        resource=target_resource,
        context={
            "workflow_id": current_workflow_id,
            "entry_point": workflow_trigger_source,
            "timestamp": current_timestamp
        }
    )
    
    # Step 3: Log the authorization decision
    log_authorization_event(
        requester=requester_identity,
        action=ai_generated_action,
        resource=target_resource,
        decision=authorization_decision.result,
        policy_version=authorization_decision.policy_id
    )
    
    # Step 4: Execute with appropriate credentials
    if authorization_decision.result == "allow":
        # Use user-scoped credentials or impersonation token
        execution_credential = credential_vault.get_scoped_token(
            for_user=requester_identity,
            [scope](/glossary/scope)=target_resource
        )
        
        result = target_resource.execute(
            action=ai_generated_action,
            credential=execution_credential,
            audit_context={
                "authorized_by": requester_identity,
                "workflow": current_workflow_id
            }
        )
        
        return {
            "status": "executed",
            "result": result,
            "authorized_identity": requester_identity
        }
    
    else:
        return {
            "status": "denied",
            "reason": authorization_decision.rationale,
            "required_permissions": authorization_decision.missing_entitlements
        }

Customizing the Template

For different entry points
If your workflow reads from multiple channels, extract identity consistently. For authenticated APIs, use the OAuth 2.0 subject claim. For email, validate the sender domain and map to your directory. For unauthenticated forms, route to manual approval or reject outright.

For complex actions
When the AI agent proposes multi-step operations, evaluate each step separately. A user authorized to read customer records isn't automatically authorized to export them to external storage. Break compound actions into atomic operations and check permissions for each.

For delegation scenarios
If your workflow needs to act beyond the requester's privileges (a manager approving on behalf of their team), make the delegation explicit. Log both the requester and the delegated authority, and require the delegated credential to be time-bound and auditable.

For legacy systems
If downstream systems can't accept user context, implement a proxy layer that enforces authorization before forwarding requests. The proxy becomes your Policy Decision Point, evaluating the requester's permissions against the target system's access rules before issuing the privileged call.

Validation Steps

Test with different identities
Send identical requests through your workflow using accounts with different privilege levels. A low-privilege user should receive a denial for actions a high-privilege user can execute. If both succeed, your workflow is acting as a confused deputy.

Verify audit correlation
Query your logs for a single workflow execution. You should see the original requester, the authorization decision, the credential used for execution, and the downstream system's confirmation. If you can't correlate these events, you can't detect unauthorized access.

Check unauthenticated paths
Submit a request through any entry point that doesn't require authentication (public forms, shared document comments, open repository issues). The workflow should reject it or route it to manual review. If it executes using the workflow's service account, you have a workflow identity hijacking vulnerability.

Simulate privilege escalation
Have a user with read-only access request a write operation through the workflow. The authorization checkpoint should deny it before the action reaches the downstream system. If the action succeeds because the workflow uses a privileged service account, your authorization boundary is broken.

Deploy this template at every point where your AI workflows translate decisions into privileged actions. The model can behave correctly and still expose your enterprise systems if the workflow doesn't enforce who's actually authorized to make the request.